69 citations · 175 across the 40 of their papers we have counts for
6 papers · 1 filter
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Aritra Bhowmick, Mert Kosan, Zexi Huang +2
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology o…
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Rishi Shah, Krishnanshu Jain, Sahil Manchanda +2
Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhib…
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Mert Kosan, Samidha Verma, Burouj Armgaan +4
Numerous explainability methods have been proposed to shed light on the inner workings of GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the i…
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Samidha Verma, Burouj Armgaan, Sourav Medya +1
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot pr…
A Survey on Explainability of Graph Neural Networks
Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma +2
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, includ…
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Zhu Wang, Sourav Medya, Sathya N. Ravi
Often, deep network models are purely inductive during training and while performing inference on unseen data. Thus, when such models are used for predictions, it is well known tha…